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dataset.py 2.41 KiB
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""create tinybert dataset"""
from enum import Enum
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.transforms.c_transforms as C
class DataType(Enum):
"""Enumerate supported dataset format"""
TFRECORD = 1
MINDRECORD = 2
def create_dataset(batch_size=32, device_num=1, rank=0, do_shuffle=True, data_dir=None,
data_type='tfrecord', seq_length=128, task_type=mstype.int32, drop_remainder=True):
"""create tinybert dataset"""
if isinstance(data_dir, list):
data_files = data_dir
else:
data_files = [data_dir]
columns_list = ["input_ids", "input_mask", "segment_ids", "label_ids"]
if data_type == 'mindrecord':
ds = de.MindDataset(data_files, columns_list=columns_list, shuffle=do_shuffle, num_shards=device_num,
shard_id=rank)
else:
ds = de.TFRecordDataset(data_files, columns_list=columns_list, shuffle=do_shuffle, num_shards=device_num,
shard_id=rank, shard_equal_rows=(device_num != 1))
if device_num == 1 and do_shuffle is True:
ds = ds.shuffle(10000)
type_cast_op = C.TypeCast(mstype.int32)
slice_op = C.Slice(slice(0, seq_length, 1))
label_type = mstype.int32 if task_type == 'classification' else mstype.float32
ds = ds.map(operations=[type_cast_op, slice_op], input_columns=["segment_ids"])
ds = ds.map(operations=[type_cast_op, slice_op], input_columns=["input_mask"])
ds = ds.map(operations=[type_cast_op, slice_op], input_columns=["input_ids"])
ds = ds.map(operations=[C.TypeCast(label_type), slice_op], input_columns=["label_ids"])
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=drop_remainder)
return ds